GPPK4PCM: pest classification model integrating growth period prior knowledge by Jianhua Zheng, Junde Lu, Yusha Fu, Ruolin Zhao, Jinfang Liu, Zhaoxi Luo, Zhijie Luo

“…The model is composed of three sub-modules where: i) A deep learning network first identifies the growth periods of pests, and this prior knowledge is then used to guide the text encoder of the CLIP pre-trained model in generating period-specific textual features. ii) A parallel deep learning network extracts visual features from pest images. iii) An efficient low-rank multimodal fusion module integrates textual and visual features through parameter-optimized tensor decomposition, significantly improving classification accuracy across pest developmental phases. …”
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Lightweight DeepLabv3+ for Semantic Food Segmentation by Bastián Muñoz, Angela Martínez-Arroyo, Constanza Acevedo, Eduardo Aguilar

“…To achieve this, the state-of-the-art DeepLabv3+ model was adapted by optimizing the backbone with the lightweight network EfficientNet-B1, replacing the Atrous Spatial Pyramid Pooling (ASPP) in the neck with Cascade Waterfall ASPP (CWASPP), and refining the encoder output using the squeeze-and-excitation attention mechanism. …”
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Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach by Olamilekan Shobayo, Sidikat Adeyemi-Longe, Olusogo Popoola, Bayode Ogunleye

“…This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. …”
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CSNet: A Remote Sensing Image Semantic Segmentation Network Based on Coordinate Attention and Skip Connections by Jiahao Li, Hongguo Zhang, Liang Chen, Binbin He, Huaixin Chen

“…Furthermore, skip connections are introduced between the encoder and decoder to directly transfer low-level features to the decoder. …”
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Lossy Infrared Image Compression Based on Wavelet Coefficient Probability Modeling and Run-Length-Enhanced Huffman Coding by Yaohua Zhu, Ya Liu, Yanghang Zhu, Mingsheng Huang, Jingyu Jiang, Yong Zhang

“…By leveraging zero-run continuity, Huf-RLC optimizes the shortest code encoding, reducing the average code length to below one bit in sparse distributions. …”
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Part of speech weighted multi-modal emotion analysis model with dynamic adjustment of semantic representation by HUA Qiang, CHEN Zhuo, ZHANG Feng, DONG Chunru

“…The PM-DS model takes natural language as the main body, and uses bidirectional encoder representation from transformer model, generalized autoregressive pre-training model for language understanding (XLNet) and a robustly optimized BERT pretraining approach (RoBERTa) to embed words into text patterns, respectively. …”
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Fine-Grained Extraction of Coastal Aquaculture Ponds From Remote Sensing Images Using an Edge-Supervised Multi-task Neural Network by Jian Qi, Min Ji, Fengxiang Jin, Jianran Xu, Hanyu Ji, Juan Wang

“…It notably enhances performance in complex environments and significantly boosts generalization capabilities by learning global structural features. First, a shared encoder–decoder architecture was constructed, leveraging large kernel depthwise separable convolution and residual optimization, thereby enhancing both local and global feature representations. …”
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Classification of psychiatry clinical notes by diagnosis: a deep learning and machine learning approach by Sergio Rubio-Martín, María Teresa García-Ordás, Antonio Serrano-García, Clara Margarita Franch-Pato, Arturo Crespo-Álvaro, José Alberto Benítez-Andrades

“…The only exception was SMOTE, which showed a positive effect specifically with Bidirectional Encoder Representations from Transformers (BERT)-based models. …”
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Developing an ICD-10 Coding Assistant: Pilot Study Using RoBERTa and GPT-4 for Term Extraction and Description-Based Code Selection by Sander Puts, Catharina M L Zegers, Andre Dekker, Iñigo Bermejo

“…A new dataset, CodiEsp-X-lead, was generated using GPT-4 to replace full-textual evidence annotations with lead term annotations. A Robustly Optimized BERT (Bidirectional Encoder Representations from Transformers) Pretraining Approach transformer model was fine-tuned for named entity recognition to extract lead terms. …”
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Detailed Architectural Design of a Multi-Head Self-Attention Model for Lithium-Ion Battery Capacity Forecasting by Juyeon Park, Gyeong Ho Lee, Jangkyum Kim, Yoon-Sik Yoo, Il-Woo Lee

“…To address variability in battery data collection, we implement robust preprocessing techniques and a sliding window method to standardize data input. Positional encoding is applied to embed sequence order information at the input stage, while residual connections and layer normalization between MHSA layers optimize the learning process. …”
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Towards representation learning of radar altimeter waveforms for sea ice surface classification by L. Happ, L. Happ, S. Patil, S. Hendricks, R. Fellegara, L. Kaleschke, A. Gerndt, A. Gerndt

“…We show that the information preserved in the latent space of an auto-encoder enhances the feature space of traditional waveform parameters, improving the subsequent classification process, when comparing our results to available sea ice charts and other remote sensing products. …”
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Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics by Laura Elena Cué La Rosa, Dario Augusto Borges Oliveira, Raul Queiroz Feitosa

“…The results show improvements of up to 30% in per-class F1 score and 12% in average F1 score compared to a baseline model that excludes temporal dependencies. …”
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RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features by Arwa A. Jamjoom, Hanen Karamti, Muhammad Umer, Shtwai Alsubai, Tai-Hoon Kim, Imran Ashraf

“…This research work employs robustly optimized bidirectional encoder representations from the transformers approach (RoBERTa), utilizing global vectors for word representation (GloVe) word embedding features. …”
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Research on green supply chain finance risk identification based on two-stage deep learning by Ying Liu, Sizhe Li, Chunmei Yu, Mingli Lv

“…In the first stage, we employ Generative Adversarial Network (GAN) to generate minority class default samples, and utilize Stacked Auto-Encoder (SAE) to extract data features with closed-form parameter calculation capability. …”
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SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation by Haijiao Yun, Qingyu Du, Ziqing Han, Mingjing Li, Le Yang, Xinyang Liu, Chao Wang, Weitian Ma

“…The Guided Multi-Scale Refiner (GMSR) further optimizes boundary details through a multi-scale semantic attention mechanism. …”
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Integrating the Prior Shape Knowledge Into Deep Model and Feature Fusion for Topologically Effective Brain Tumor Segmentation by Salma Asif, Ahmad Raza Shahid, Kiran Aftab, Syed Ather Enam

“…Our results highlight better segmentation performance compared to the existing state-of-the-art methods.…”
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Transformer-Based Motion Predictor for Multi-Dancer Tracking in Non-Linear Movements of Dancesport Performance by Zhiling Wang

“…Unlike conventional tracking methods that integrate appearance features, MDSTT processes historical bounding box trajectories through a transformer encoder, capturing both long-range and short-term spatio-temporal dependencies while mitigating occlusion-induced identity switches. …”
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NoiseAugmentNet-HHO: Enhancing Histopathological Image Classification Through Noise Augmentation by Prem Purusottam Jena, Debahuti Mishra, Kaberi Das, Sashikala Mishra

“…Key innovations include NoiseAugmentNet-HHO, which integrates Harris Hawks optimization (HHO) with VGG16, ResNet50, and deep CNN (DCNN). …”
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CVT-HNet: a fusion model for recognizing perianal fistulizing Crohn’s disease based on CNN and ViT by Lanlan Li, Ziyue Wang, Chongyang Wang, Tao Chen, Ke Deng, Hong’an Wei, Dabiao Wang, Juan Li, Heng Zhang

“…In addition, the MobileNetV2 with Coordinate Attention mechanism and encoder modules are optimized to improve the precision of detecting anal fistulas. …”
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AED-Net: A High-Resolution Remote Sensing Image Road Extraction Method Integrating Atrous Spatial Pyramid Pooling and Efficient Channel Attention Mechanism by Jintong Ren, Lizhi Liu, Zixuan Xia, Yang Liu

“…Similarly, on the Ottawa Road dataset, AED-Net exhibits superior performance compared to classical semantic segmentation networks such as SegNet, U-Net, and Deeplab V3+, achieving an OA of 98.83% and an MIoU of 88.74%. …”
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